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Science as an Anomaly-Driven Enterprise: A Computational Approach to Generating Acceptable Theory Revisions in the Face of Anomalous Data

Bridewell, Will (2005) Science as an Anomaly-Driven Enterprise: A Computational Approach to Generating Acceptable Theory Revisions in the Face of Anomalous Data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Anomalous data lead to scientific discoveries. Although machine learning systems can be forced to resolve anomalous data, these systems use general learning algorithms to do so. To determine whether anomaly-driven approaches to discovery produce more accurate models than the standard approaches, we built a program called Kalpana. We also used Kalpana to explore means for identifying those anomaly resolutions that are acceptable to domain experts. Our experiments indicated that anomaly-driven approaches can lead to a richer set of model revisions than standard methods. Additionally we identified semantic and syntactic measures that are significantly correlated with the acceptability of model revisions. These results suggest that by interpreting data within the context of a model and by interpreting model revisions within the context of domain knowledge, discovery systems can more readily suggest accurate and acceptable anomaly resolutions.

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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Bridewell, Willwillb@csli.stanford.edu
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairBuchanan, Brucebuchanan@cs.pitt.eduBBUCHANA
Committee CoChairHauskrecht, Milosmilos@cs.pitt.eduMILOS
Committee MemberCooper, Greggfc@cbmi.upmc.edu
Committee MemberAshley, Kevinashley@pitt.eduASHLEY
Date: 3 February 2005
Date Type: Completion
Defense Date: 17 September 2004
Approval Date: 3 February 2005
Submission Date: 8 December 2004
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: anomaly resolution; artificial intelligence; hypothesis formation; machine learning; plausibility; scientific discovery; theory revision
Other ID: http://etd.library.pitt.edu/ETD/available/etd-12082004-180933/, etd-12082004-180933
Date Deposited: 10 Nov 2011 20:09
Last Modified: 15 Nov 2016 13:53
URI: http://d-scholarship.pitt.edu/id/eprint/10179

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